Changes in the European Energy Market due the Russia-Ukraine War
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Author
Gavin Ulrich
Published
December 8, 2024
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Intro -
Research Question - How has the European energy market been impacted by the ending of Russian energy export due to the Ukraine war? How has the market changed through both domestic production and international exports?
Data Sources - A large portion of our data comes directly from the European Union through the information gained through Eurostat. Eurostat is the statistics office of the EU and provides data on Europe through an official EU website. This is data produced by the EU itself, and although its process internally it is a primary source of the data. The EU is a reputable source, as it is a major international agreement that is considered very reputable. I believe that the data is reputable because the EU uses the data it collects to make informed decisions. The data needs to be accurate in order to properly make decisions, so therefore it makes more sense for this data to be objective.
Some of our data also comes from independent sources, generally independent climate or energy research groups. These groups also tend to be reputable, however there can be issues with the data due to political reasons. Certain research groups can be funded in order to provide data in order to support political issues, and especially with energy data on energy production and climate impact which both have a strong influence on political discussions. However despite these issues I believe our sources are reliable and aren’t affected due to how the data they record is more objective and reports usage and exports rather than scientific finds. These sources are more focused around market and export data, which in a similar case to the EUs datasets need to be accurate in order to have a good understanding of how the market is changing.
#> # A tibble: 10 × 13
#> dataflow last_update freq product nrg_cons unit tax currency geo
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 2 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 3 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 4 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 5 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 6 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 7 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 8 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 9 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> 10 ESTAT:NRG_PC_2… 27/10/24 2… S 4100 GJ20-199 KWH X_TAX EUR EA
#> # ℹ 4 more variables: time_period <chr>, obs_value <dbl>, obs_flag <chr>,
#> # color <chr>
ggplot(euro_area) +geom_bar(aes(x = time_period, y = obs_value), stat ="identity", width =0.8, show.legend =FALSE) +labs(title ="Gas Cost Per Household in Euro Area",subtitle ="Exluding Taxes and Levies",x ="Time Period",y ="Gas Cost kWh per Euro",caption ="Source of data:Eurostat") +theme_void()+theme(plot.title =element_text(size =20),plot.subtitle =element_text(size =12), axis.text.x =element_text(angle =45, hjust =1, size =8, vjust =1), axis.title.y =element_text(angle =90, hjust =0.5, size =12, vjust =1), axis.title.x =element_text(size =12), axis.text.y =element_text(size =8),plot.margin =margin(1, 1, 3, 1) ) +geom_vline(xintercept =5.8, linetype ="solid", color ="red", size =1) +annotate("text", x =4.8, y = .08,label ="Start of War", angle =0, vjust =-0.5, size =5)
Russia played a major role in the European energy market as a major exporter of natural gas. Energy sources from Russia was found throughout Europe, helping
Immediate Impact
```{r
route_data <- read_xlsx(here::here(‘data_raw’, ‘LNG plot data 2024-12-04.xlsx’))
long_import <- long_import %>% group_by(dates) %>% # Group by ‘date’ to calculate and reorder for each date mutate(country = factor(country, levels = country[order(value, decreasing = TRUE)])) %>% ungroup()
#> # A tibble: 118 × 18
#> area country_code date area_type continent ember_region eu oecd
#> <chr> <chr> <date> <chr> <chr> <chr> <dbl> <dbl>
#> 1 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 2 EU <NA> 2015-02-01 Region <NA> <NA> NA NA
#> 3 EU <NA> 2015-03-01 Region <NA> <NA> NA NA
#> 4 EU <NA> 2015-04-01 Region <NA> <NA> NA NA
#> 5 EU <NA> 2015-05-01 Region <NA> <NA> NA NA
#> 6 EU <NA> 2015-06-01 Region <NA> <NA> NA NA
#> 7 EU <NA> 2015-07-01 Region <NA> <NA> NA NA
#> 8 EU <NA> 2015-08-01 Region <NA> <NA> NA NA
#> 9 EU <NA> 2015-09-01 Region <NA> <NA> NA NA
#> 10 EU <NA> 2015-10-01 Region <NA> <NA> NA NA
#> # ℹ 108 more rows
#> # ℹ 10 more variables: g20 <dbl>, g7 <dbl>, asean <dbl>, category <chr>,
#> # subcategory <chr>, variable <chr>, unit <chr>, value <dbl>,
#> # yo_y_absolute_change <dbl>, yo_y_percent_change <dbl>
total_prodution <- eu_total %>%ggplot(aes(x = date, y = value, color = variable))+geom_line() +theme_minimal() +labs(title ="Total EU Domestic Electriciy Generation By Type", x ="Date", y ="Terrawatt Hours (TWh)",caption ="Source: Ember") +scale_color_viridis(discrete =TRUE, option ="plasma") +theme(legend.position ="none")total_prodution
eu_energy_source
#> # A tibble: 1,062 × 18
#> area country_code date area_type continent ember_region eu oecd
#> <chr> <chr> <date> <chr> <chr> <chr> <dbl> <dbl>
#> 1 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 2 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 3 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 4 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 5 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 6 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 7 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 8 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 9 EU <NA> 2015-01-01 Region <NA> <NA> NA NA
#> 10 EU <NA> 2015-02-01 Region <NA> <NA> NA NA
#> # ℹ 1,052 more rows
#> # ℹ 10 more variables: g20 <dbl>, g7 <dbl>, asean <dbl>, category <chr>,
#> # subcategory <chr>, variable <chr>, unit <chr>, value <dbl>,
#> # yo_y_absolute_change <dbl>, yo_y_percent_change <dbl>